The Association of Saliva Cytokines and Pediatric Sports-Related Concussion Outcomes.

OBJECTIVES: This study aimed to explore cytokine alterations following pediatric sports-related concussion (SRC) and whether a specific cytokine profile could predict symptom burden and time to return to sports (RTS). SETTING: Sports Medicine Clinic. PARTICIPANTS: Youth ice hockey participants (aged 12-17 years) were recruited prior to the 2013-2016 hockey season. DESIGN: Prospective exploratory cohort study. MAIN MEASURE: Following SRC, saliva samples were collected and a Sport Concussion Assessment Tool version 3 (SCAT3) was administered within 72 hours of injury and analyzed for cytokines. Additive regression of decision stumps was used to model symptom burden and length to RTS based on cytokine and clinical features. RRelieFF feature selection was used to determine the predictive value of each cytokine and clinical feature, as well as to identify the optimal cytokine profile for the symptom burden and RTS. RESULTS: Thirty-six participants provided samples post-SRC (81% male; age 14.4 ± 1.3 years). Of these, 10 features, sex, number of previous concussions, and 8 cytokines, were identified to lead to the best prediction of symptom severity (r = 0.505, P = .002), while 12 cytokines, age, and history of previous concussions predicted the number of symptoms best (r = 0.637, P < .001). The prediction of RTS led to the worst results, requiring 21 cytokines, age, sex, and number of previous concussions as features (r = -0.320, P = .076). CONCLUSIONS: In pediatric ice hockey participants following SRC, there is evidence of saliva cytokine profiles that are associated with increased symptom burden. However, further studies are needed.

Automatic classification of major depression disorder using arterial spin labeling MRI perfusion measurements.

AIM: Neuroimaging-based multivariate pattern-recognition methods have been successfully used to develop diagnostic algorithms to distinguish patients with major depressive disorder (MDD) from healthy controls (HC). We developed and evaluated the accuracy of a multivariate classification method for the differentiation of MDD and HC using cerebral blood flow (CBF) features measured by non-invasive arterial spin labeling (ASL) MRI. METHODS: Twenty-two medication-free patients with the diagnosis of MDD based on DSM-IV criteria and 22 HC underwent pseudo-continuous 3-D-ASL imaging to assess CBF. Using an atlas-based approach, regional CBF was determined in various brain regions and used together with sex and age as classification features. A linear kernel support vector machine was used for feature ranking and selection as well as for the classification of patients with MDD and HC. Permutation testing was used to test for significance of the classification results. RESULTS: The automatic classifier based on CBF features showed a statistically significant accuracy of 77.3% (P = 0.004) with a specificity of 80% and sensitivity of 75% for classification of MDD versus HC. The features that contributed to the classification were sex and regional CBF of the cortical, limbic, and paralimbic regions. CONCLUSION: Machine-learning models based on CBF measurements are capable of differentiating MDD from HC with high accuracy. The use of larger study cohorts and inclusion of other imaging measures may improve the performance of the classifier to achieve the accuracy required for clinical application.